Working with 3D Gathers¶

Gathers or pre-stack data are common in seismic interpretation and processing. In this example we will load gather data by finding and specifying the offset byte location. Learn different ways to plot and select offset data. As well as perform a simple mute and trace stack to reduce the offset dimension.

In [1]:

import pathlib
from IPython.display import display
import pandas as pd
import xarray as xr
import numpy as np
from segysak.segy import segy_loader, segy_header_scan
import matplotlib.pyplot as plt

import pathlib from IPython.display import display import pandas as pd import xarray as xr import numpy as np from segysak.segy import segy_loader, segy_header_scan import matplotlib.pyplot as plt

This example uses a subset of the Penobscot 3D with data exported from the OpendTect project.

First we scan the data to determine which byte locations contain the relevant information. We will need to provide a byte location for the offset variable so a 4th dimension can be created when loaded the data.

In [2]:

segy_file = pathlib.Path("data/3D_gathers_pstm_nmo.sgy")
with pd.option_context("display.max_rows", 100):
    display(segy_header_scan(segy_file))

segy_file = pathlib.Path("data/3D_gathers_pstm_nmo.sgy") with pd.option_context("display.max_rows", 100): display(segy_header_scan(segy_file))

  0%|          | 0.00/1.00k [00:00<?, ? traces/s]

100%|██████████| 1.00k/1.00k [00:00<00:00, 21.0k traces/s]

	byte_loc	count	mean	std	min	25%	50%	75%	max
TRACE_SEQUENCE_LINE	1	1000.0	2.797410e+02	186.100369	1.0	125.75	250.5	421.25	671.0
TRACE_SEQUENCE_FILE	5	1000.0	3.055600e+01	17.664623	1.0	15.00	30.0	46.00	61.0
FieldRecord	9	1000.0	1.290329e+03	0.470085	1290.0	1290.00	1290.0	1291.00	1291.0
TraceNumber	13	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
EnergySourcePoint	17	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
CDP	21	1000.0	1.154085e+03	3.039245	1150.0	1152.00	1154.0	1156.00	1160.0
CDP_TRACE	25	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TraceIdentificationCode	29	1000.0	1.000000e+00	0.000000	1.0	1.00	1.0	1.00	1.0
NSummedTraces	31	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
NStackedTraces	33	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
DataUse	35	1000.0	1.000000e+00	0.000000	1.0	1.00	1.0	1.00	1.0
offset	37	1000.0	1.652800e+03	883.231146	175.0	875.00	1625.0	2425.00	3175.0
ReceiverGroupElevation	41	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceSurfaceElevation	45	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceDepth	49	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
ReceiverDatumElevation	53	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceDatumElevation	57	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceWaterDepth	61	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GroupWaterDepth	65	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
ElevationScalar	69	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceGroupScalar	71	1000.0	-1.000000e+01	0.000000	-10.0	-10.00	-10.0	-10.00	-10.0
SourceX	73	1000.0	7.337142e+06	685.656940	7336198.0	7336642.00	7337085.0	7337586.00	7338472.0
SourceY	77	1000.0	4.895109e+07	333.157506	48950580.0	48950812.00	48951044.0	48951276.00	48951739.0
GroupX	81	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GroupY	85	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
CoordinateUnits	89	1000.0	1.000000e+00	0.000000	1.0	1.00	1.0	1.00	1.0
WeatheringVelocity	91	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SubWeatheringVelocity	93	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceUpholeTime	95	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GroupUpholeTime	97	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceStaticCorrection	99	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GroupStaticCorrection	101	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TotalStaticApplied	103	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
LagTimeA	105	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
LagTimeB	107	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
DelayRecordingTime	109	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
MuteTimeStart	111	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
MuteTimeEND	113	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TRACE_SAMPLE_COUNT	115	1000.0	7.510000e+02	0.000000	751.0	751.00	751.0	751.00	751.0
TRACE_SAMPLE_INTERVAL	117	1000.0	4.000000e+03	0.000000	4000.0	4000.00	4000.0	4000.00	4000.0
GainType	119	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
InstrumentGainConstant	121	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
InstrumentInitialGain	123	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
Correlated	125	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SweepFrequencyStart	127	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SweepFrequencyEnd	129	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SweepLength	131	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SweepType	133	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SweepTraceTaperLengthStart	135	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SweepTraceTaperLengthEnd	137	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TaperType	139	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
AliasFilterFrequency	141	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
AliasFilterSlope	143	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
NotchFilterFrequency	145	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
NotchFilterSlope	147	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
LowCutFrequency	149	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
HighCutFrequency	151	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
LowCutSlope	153	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
HighCutSlope	155	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
YearDataRecorded	157	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
DayOfYear	159	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
HourOfDay	161	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
MinuteOfHour	163	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SecondOfMinute	165	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TimeBaseCode	167	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TraceWeightingFactor	169	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GeophoneGroupNumberRoll1	171	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GeophoneGroupNumberFirstTraceOrigField	173	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GeophoneGroupNumberLastTraceOrigField	175	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
GapSize	177	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
OverTravel	179	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
CDP_X	181	1000.0	7.337142e+06	685.656940	7336198.0	7336642.00	7337085.0	7337586.00	7338472.0
CDP_Y	185	1000.0	4.895109e+07	333.157506	48950580.0	48950812.00	48951044.0	48951276.00	48951739.0
INLINE_3D	189	1000.0	1.290329e+03	0.470085	1290.0	1290.00	1290.0	1291.00	1291.0
CROSSLINE_3D	193	1000.0	1.154085e+03	3.039245	1150.0	1152.00	1154.0	1156.00	1160.0
ShotPoint	197	1000.0	3.055600e+01	17.664623	1.0	15.00	30.0	46.00	61.0
ShotPointScalar	201	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TraceValueMeasurementUnit	203	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TransductionConstantMantissa	205	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TransductionConstantPower	209	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TransductionUnit	211	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
TraceIdentifier	213	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
ScalarTraceHeader	215	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceType	217	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceEnergyDirectionMantissa	219	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceEnergyDirectionExponent	223	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceMeasurementMantissa	225	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceMeasurementExponent	229	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceMeasurementUnit	231	1000.0	0.000000e+00	0.000000	0.0	0.00	0.0	0.00	0.0

In [3]:

penobscot_3d_gath = segy_loader(
    segy_file, iline=189, xline=193, cdp_x=181, cdp_y=185, offset=37
)

penobscot_3d_gath = segy_loader( segy_file, iline=189, xline=193, cdp_x=181, cdp_y=185, offset=37 )

  0%|          | 0.00/7.38k [00:00<?, ? traces/s]

100%|██████████| 7.38k/7.38k [00:00<00:00, 21.0k traces/s]

100%|██████████| 7.38k/7.38k [00:00<00:00, 21.0k traces/s]

Loading as 3D
Fast direction is INLINE_3D

Converting SEGY:   0%|          | 0.00/7.38k [00:00<?, ? traces/s]

Converting SEGY:   7%|▋         | 507/7.38k [00:00<00:01, 5.06k traces/s]

Converting SEGY:  14%|█▍        | 1.04k/7.38k [00:00<00:01, 5.24k traces/s]

Converting SEGY:  22%|██▏       | 1.59k/7.38k [00:00<00:01, 5.32k traces/s]

Converting SEGY:  29%|██▉       | 2.14k/7.38k [00:00<00:00, 5.39k traces/s]

Converting SEGY:  36%|███▋      | 2.69k/7.38k [00:00<00:00, 5.41k traces/s]

Converting SEGY:  44%|████▍     | 3.23k/7.38k [00:00<00:00, 5.44k traces/s]

Converting SEGY:  51%|█████▏    | 3.78k/7.38k [00:00<00:00, 5.45k traces/s]

Converting SEGY:  59%|█████▊    | 4.33k/7.38k [00:00<00:00, 5.45k traces/s]

Converting SEGY:  66%|██████▌   | 4.88k/7.38k [00:00<00:00, 5.45k traces/s]

Converting SEGY:  73%|███████▎  | 5.42k/7.38k [00:01<00:00, 5.46k traces/s]

Converting SEGY:  81%|████████  | 5.97k/7.38k [00:01<00:00, 5.45k traces/s]

Converting SEGY:  88%|████████▊ | 6.52k/7.38k [00:01<00:00, 5.46k traces/s]

Converting SEGY:  96%|█████████▌| 7.06k/7.38k [00:01<00:00, 5.46k traces/s]

Converting SEGY: 100%|██████████| 7.38k/7.38k [00:01<00:00, 5.43k traces/s]

Note that the loaded Dataset has four dimensions with the additional dimension labeled offset. There are 61 offsets in this dataset or 61 traces per inline and xline location.

In [4]:

display(penobscot_3d_gath)
print(penobscot_3d_gath.offset.values)

display(penobscot_3d_gath) print(penobscot_3d_gath.offset.values)

<xarray.Dataset> Size: 22MB
Dimensions:  (iline: 11, xline: 11, twt: 751, offset: 61)
Coordinates:
  * iline    (iline) uint16 22B 1290 1291 1292 1293 1294 ... 1297 1298 1299 1300
  * xline    (xline) uint16 22B 1150 1151 1152 1153 1154 ... 1157 1158 1159 1160
  * twt      (twt) float64 6kB 0.0 4.0 8.0 12.0 ... 2.992e+03 2.996e+03 3e+03
  * offset   (offset) uint16 122B 175 225 275 325 375 ... 3025 3075 3125 3175
    cdp_x    (iline, xline, offset) float32 30kB 7.336e+05 ... 7.338e+05
    cdp_y    (iline, xline, offset) float32 30kB 4.895e+06 ... 4.895e+06
Data variables:
    data     (iline, xline, twt, offset) float32 22MB 0.0 0.0 ... 670.1
Attributes: (12/13)
    ns:                  None
    sample_rate:         4.0
    text:                C01\nC02\nC03\nC04\nC05\nC06\nC07  Positions accordi...
    measurement_system:  m
    d3_domain:           None
    epsg:                None
    ...                  ...
    corner_points_xy:    None
    source_file:         3D_gathers_pstm_nmo.sgy
    srd:                 None
    datatype:            None
    percentiles:         [-4084.319948142755, -3961.308399237772, -1082.87965...
    coord_scalar:        -10.0

xarray.Dataset

• Dimensions:
  • iline: 11
  • xline: 11
  • twt: 751
  • offset: 61
• Coordinates: (6)
  • iline
    (iline)
    uint16
    1290 1291 1292 ... 1298 1299 1300

    array([1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300],
          dtype=uint16)

  • xline
    (xline)
    uint16
    1150 1151 1152 ... 1158 1159 1160

    array([1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160],
          dtype=uint16)

  • twt
    (twt)
    float64
    0.0 4.0 8.0 ... 2.996e+03 3e+03

    array([   0.,    4.,    8., ..., 2992., 2996., 3000.])

  • offset
    (offset)
    uint16
    175 225 275 325 ... 3075 3125 3175

    array([ 175,  225,  275,  325,  375,  425,  475,  525,  575,  625,  675,  725,
            775,  825,  875,  925,  975, 1025, 1075, 1125, 1175, 1225, 1275, 1325,
           1375, 1425, 1475, 1525, 1575, 1625, 1675, 1725, 1775, 1825, 1875, 1925,
           1975, 2025, 2075, 2125, 2175, 2225, 2275, 2325, 2375, 2425, 2475, 2525,
           2575, 2625, 2675, 2725, 2775, 2825, 2875, 2925, 2975, 3025, 3075, 3125,
           3175], dtype=uint16)

  • cdp_x
    (iline, xline, offset)
    float32
    7.336e+05 7.336e+05 ... 7.338e+05

    array([[[733625.6 , 733625.6 , 733625.6 , ..., 733625.6 , 733625.6 ,
             733625.6 ],
            [733647.8 , 733647.8 , 733647.8 , ..., 733647.8 , 733647.8 ,
             733647.8 ],
            [733669.94, 733669.94, 733669.94, ..., 733669.94, 733669.94,
             733669.94],
            ...,
            [733802.94, 733802.94, 733802.94, ..., 733802.94, 733802.94,
             733802.94],
            [733825.1 , 733825.1 , 733825.1 , ..., 733825.1 , 733825.1 ,
             733825.1 ],
            [733847.2 , 733847.2 , 733847.2 , ..., 733847.2 , 733847.2 ,
             733847.2 ]],
    
           [[733619.8 , 733619.8 , 733619.8 , ..., 733619.8 , 733619.8 ,
             733619.8 ],
            [733642.  , 733642.  , 733642.  , ..., 733642.  , 733642.  ,
             733642.  ],
            [733664.2 , 733664.2 , 733664.2 , ..., 733664.2 , 733664.2 ,
             733664.2 ],
    ...
            [733750.8 , 733750.8 , 733750.8 , ..., 733750.8 , 733750.8 ,
             733750.8 ],
            [733772.94, 733772.94, 733772.94, ..., 733772.94, 733772.94,
             733772.94],
            [733795.1 , 733795.1 , 733795.1 , ..., 733795.1 , 733795.1 ,
             733795.1 ]],
    
           [[733567.7 , 733567.7 , 733567.7 , ..., 733567.7 , 733567.7 ,
             733567.7 ],
            [733589.94, 733589.94, 733589.94, ..., 733589.94, 733589.94,
             733589.94],
            [733612.  , 733612.  , 733612.  , ..., 733612.  , 733612.  ,
             733612.  ],
            ...,
            [733745.  , 733745.  , 733745.  , ..., 733745.  , 733745.  ,
             733745.  ],
            [733767.1 , 733767.1 , 733767.1 , ..., 733767.1 , 733767.1 ,
             733767.1 ],
            [733789.3 , 733789.3 , 733789.3 , ..., 733789.3 , 733789.3 ,
             733789.3 ]]], dtype=float32)

  • cdp_y
    (iline, xline, offset)
    float32
    4.895e+06 4.895e+06 ... 4.895e+06

    array([[[4895058. , 4895058. , 4895058. , ..., 4895058. , 4895058. ,
             4895058. ],
            [4895069.5, 4895069.5, 4895069.5, ..., 4895069.5, 4895069.5,
             4895069.5],
            [4895081.5, 4895081.5, 4895081.5, ..., 4895081.5, 4895081.5,
             4895081.5],
            ...,
            [4895151. , 4895151. , 4895151. , ..., 4895151. , 4895151. ,
             4895151. ],
            [4895162.5, 4895162.5, 4895162.5, ..., 4895162.5, 4895162.5,
             4895162.5],
            [4895174. , 4895174. , 4895174. , ..., 4895174. , 4895174. ,
             4895174. ]],
    
           [[4895069.5, 4895069.5, 4895069.5, ..., 4895069.5, 4895069.5,
             4895069.5],
            [4895081. , 4895081. , 4895081. , ..., 4895081. , 4895081. ,
             4895081. ],
            [4895092.5, 4895092.5, 4895092.5, ..., 4895092.5, 4895092.5,
             4895092.5],
    ...
            [4895250.5, 4895250.5, 4895250.5, ..., 4895250.5, 4895250.5,
             4895250.5],
            [4895262. , 4895262. , 4895262. , ..., 4895262. , 4895262. ,
             4895262. ],
            [4895273.5, 4895273.5, 4895273.5, ..., 4895273.5, 4895273.5,
             4895273.5]],
    
           [[4895169. , 4895169. , 4895169. , ..., 4895169. , 4895169. ,
             4895169. ],
            [4895180.5, 4895180.5, 4895180.5, ..., 4895180.5, 4895180.5,
             4895180.5],
            [4895192. , 4895192. , 4895192. , ..., 4895192. , 4895192. ,
             4895192. ],
            ...,
            [4895261.5, 4895261.5, 4895261.5, ..., 4895261.5, 4895261.5,
             4895261.5],
            [4895273. , 4895273. , 4895273. , ..., 4895273. , 4895273. ,
             4895273. ],
            [4895285. , 4895285. , 4895285. , ..., 4895285. , 4895285. ,
             4895285. ]]], dtype=float32)

• Data variables: (1)
  • data
    (iline, xline, twt, offset)
    float32
    0.0 0.0 0.0 ... 1.104e+03 670.1

    array([[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             ...,
             [-1.23879443e+03, -1.58738135e+03, -1.29027637e+03, ...,
               1.15945190e+03,  2.39706528e+02,  6.10369385e+02],
             [-2.12930640e+03, -1.65743652e+03, -1.12646802e+03, ...,
               5.31902100e+02,  7.50732422e+02,  1.63174133e+01],
             [-1.54500171e+03, -6.30833252e+02, -5.51544678e+02, ...,
              -3.74271484e+02,  6.95094971e+02, -5.48833008e+02]],
    
            [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
    ...
               1.85541611e+02, -8.72761078e+01,  5.61426270e+02],
             [ 4.05418701e+02, -7.06164062e+02, -3.18643555e+02, ...,
               4.05202881e+02,  5.99214844e+02,  9.09387451e+02],
             [ 4.10260010e+02, -2.20607529e+02, -3.29194336e+02, ...,
               4.69453369e+02,  9.48709229e+02,  7.38079102e+02]],
    
            [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
               0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
             ...,
             [-7.89181213e+01,  2.79263306e+01,  1.42925537e+03, ...,
               9.92185059e+02,  3.37179199e+02,  1.15724780e+03],
             [ 1.33805688e+03,  2.26713165e+02,  7.28656006e+02, ...,
               4.84429443e+02,  6.98447754e+02,  1.34752148e+03],
             [ 1.70246509e+03,  6.15660400e+02, -1.10133026e+02, ...,
               1.33893036e+02,  1.10410913e+03,  6.70144775e+02]]]],
          dtype=float32)

• Indexes: (4)
  • iline
    PandasIndex

    PandasIndex(Index([1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300], dtype='uint16', name='iline'))

  • xline
    PandasIndex

    PandasIndex(Index([1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160], dtype='uint16', name='xline'))

  • twt
    PandasIndex

    PandasIndex(Index([   0.0,    4.0,    8.0,   12.0,   16.0,   20.0,   24.0,   28.0,   32.0,
             36.0,
           ...
           2964.0, 2968.0, 2972.0, 2976.0, 2980.0, 2984.0, 2988.0, 2992.0, 2996.0,
           3000.0],
          dtype='float64', name='twt', length=751))

  • offset
    PandasIndex

    PandasIndex(Index([ 175,  225,  275,  325,  375,  425,  475,  525,  575,  625,  675,  725,
            775,  825,  875,  925,  975, 1025, 1075, 1125, 1175, 1225, 1275, 1325,
           1375, 1425, 1475, 1525, 1575, 1625, 1675, 1725, 1775, 1825, 1875, 1925,
           1975, 2025, 2075, 2125, 2175, 2225, 2275, 2325, 2375, 2425, 2475, 2525,
           2575, 2625, 2675, 2725, 2775, 2825, 2875, 2925, 2975, 3025, 3075, 3125,
           3175],
          dtype='uint16', name='offset'))

• Attributes: (13)

  ns :

  None

  sample_rate :

  4.0

  text :

  C01 C02 C03 C04 C05 C06 C07 Positions according to the SEG-Y Rev. 1 standard. C08 C09 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 C33 C34 C35 C36 C37 C38 C39 SEG Y REV1 C40 END TEXTUAL HEADER

  measurement_system :

  m

  d3_domain :

  None

  epsg :

  None

  corner_points :

  None

  corner_points_xy :

  None

  source_file :

  3D_gathers_pstm_nmo.sgy

  srd :

  None

  datatype :

  None

  percentiles :

  [-4084.319948142755, -3961.308399237772, -1082.8796543127435, 2.3373340317115076e-14, 1092.8330121165316, 4368.894830403378, 4831.711790490108]

  coord_scalar :

  -10.0

[ 175  225  275  325  375  425  475  525  575  625  675  725  775  825
  875  925  975 1025 1075 1125 1175 1225 1275 1325 1375 1425 1475 1525
 1575 1625 1675 1725 1775 1825 1875 1925 1975 2025 2075 2125 2175 2225
 2275 2325 2375 2425 2475 2525 2575 2625 2675 2725 2775 2825 2875 2925
 2975 3025 3075 3125 3175]

Lets check that the data looks OK for a couple of offsets. We've only got a small dataset of 11x11 traces so the seismic will look at little odd at this scale.

In [5]:

fig, axs = plt.subplots(ncols=2, figsize=(20, 10))

penobscot_3d_gath.isel(iline=5, offset=0).data.T.plot(
    yincrease=False, ax=axs[0], vmax=5000
)
penobscot_3d_gath.isel(xline=5, offset=0).data.T.plot(
    yincrease=False, ax=axs[1], vmax=5000
)

fig, axs = plt.subplots(ncols=2, figsize=(20, 10)) penobscot_3d_gath.isel(iline=5, offset=0).data.T.plot( yincrease=False, ax=axs[0], vmax=5000 ) penobscot_3d_gath.isel(xline=5, offset=0).data.T.plot( yincrease=False, ax=axs[1], vmax=5000 )

Out[5]:

<matplotlib.collections.QuadMesh at 0x7fbae6685720>

Plotting Gathers Sequentially¶

Plotting of gathers is often done in a stacked way, displaying sequential gathers along a common dimension, usually inline or crossline. Xarray provides the stack method which can be used to stack labelled dimensions together.

In [6]:

fig, axs = plt.subplots(figsize=(20, 10))
axs.imshow(
    penobscot_3d_gath.isel(iline=0)
    .data.stack(stacked_offset=("xline", "offset"))
    .values,
    vmin=-5000,
    vmax=5000,
    cmap="seismic",
    aspect="auto",
)

fig, axs = plt.subplots(figsize=(20, 10)) axs.imshow( penobscot_3d_gath.isel(iline=0) .data.stack(stacked_offset=("xline", "offset")) .values, vmin=-5000, vmax=5000, cmap="seismic", aspect="auto", )

Out[6]:

<matplotlib.image.AxesImage at 0x7fbb2c9a7b20>

One can easily create a common offset stack by reversing the stacked dimension arguments "offset" and "xline".

In [7]:

fig, axs = plt.subplots(figsize=(20, 10))
axs.imshow(
    penobscot_3d_gath.isel(iline=0)
    .data.stack(stacked_offset=("offset", "xline"))
    .values,
    vmin=-5000,
    vmax=5000,
    cmap="seismic",
    aspect="auto",
)

fig, axs = plt.subplots(figsize=(20, 10)) axs.imshow( penobscot_3d_gath.isel(iline=0) .data.stack(stacked_offset=("offset", "xline")) .values, vmin=-5000, vmax=5000, cmap="seismic", aspect="auto", )

Out[7]:

<matplotlib.image.AxesImage at 0x7fbae6447f70>

Arbitrary line extraction on Gathers¶

Arbitrary line slicing of gathers based upon coordinates is also possible. Lets create a line that crosses the 3D.

In [8]:

arb_line = np.array([(733600, 733850), (4895180.0, 4895180.0)])

ax = penobscot_3d_gath.seis.plot_bounds()
ax.plot(arb_line[0, :], arb_line[1, :], label="arb_line")
plt.legend()

arb_line = np.array([(733600, 733850), (4895180.0, 4895180.0)]) ax = penobscot_3d_gath.seis.plot_bounds() ax.plot(arb_line[0, :], arb_line[1, :], label="arb_line") plt.legend()

Out[8]:

<matplotlib.legend.Legend at 0x7fbae62c7340>

Here we need to think carefully about the bin_spacing_hint. We also don't want to interpolate the gathers, so we use xysel_method="nearest".

In [9]:

penobscot_3d_gath_arb = penobscot_3d_gath.seis.interp_line(
    arb_line[0, :], arb_line[1, :], bin_spacing_hint=30, xysel_method="nearest"
)

penobscot_3d_gath_arb = penobscot_3d_gath.seis.interp_line( arb_line[0, :], arb_line[1, :], bin_spacing_hint=30, xysel_method="nearest" )

In [10]:

fig, axs = plt.subplots(figsize=(20, 10))
axs.imshow(
    penobscot_3d_gath_arb.data.stack(
        stacked_offset=(
            "cdp",
            "offset",
        )
    ).values,
    vmin=-5000,
    vmax=5000,
    cmap="seismic",
    aspect="auto",
)

fig, axs = plt.subplots(figsize=(20, 10)) axs.imshow( penobscot_3d_gath_arb.data.stack( stacked_offset=( "cdp", "offset", ) ).values, vmin=-5000, vmax=5000, cmap="seismic", aspect="auto", )

Out[10]:

<matplotlib.image.AxesImage at 0x7fbae608f340>

Muting and Stacking Gathers¶

Using one of our gathers let's define a mute function before we stack the data.

In [11]:

fig, axs = plt.subplots(ncols=2, figsize=(20, 10))

penobscot_3d_gath.isel(iline=5, xline=0).data.plot(
    yincrease=False, ax=axs[0], vmax=5000
)

# the mute relates the offset to an expected twt, let's just use a linear mute for this example
mute = penobscot_3d_gath.offset * 0.6 + 300
# and then we can plot it up
mute.plot(ax=axs[0], color="k")
# apply the mute to the volume
penobscot_3d_gath_muted = penobscot_3d_gath.where(penobscot_3d_gath.twt > mute)

# muted
penobscot_3d_gath_muted.isel(iline=5, xline=0).data.plot(
    yincrease=False, ax=axs[1], vmax=5000
)

fig, axs = plt.subplots(ncols=2, figsize=(20, 10)) penobscot_3d_gath.isel(iline=5, xline=0).data.plot( yincrease=False, ax=axs[0], vmax=5000 ) # the mute relates the offset to an expected twt, let's just use a linear mute for this example mute = penobscot_3d_gath.offset * 0.6 + 300 # and then we can plot it up mute.plot(ax=axs[0], color="k") # apply the mute to the volume penobscot_3d_gath_muted = penobscot_3d_gath.where(penobscot_3d_gath.twt > mute) # muted penobscot_3d_gath_muted.isel(iline=5, xline=0).data.plot( yincrease=False, ax=axs[1], vmax=5000 )

Out[11]:

<matplotlib.collections.QuadMesh at 0x7fbae4ed7970>

Stacking is the process of averaging the gathers for constant time to create a single trace per inline and crossline location.

In [12]:

fig, axs = plt.subplots(ncols=3, figsize=(20, 10))

plot_kwargs = dict(vmax=5000, interpolation="bicubic", yincrease=False)

# compare with the zero offset trace and use imshow for interpolation
penobscot_3d_gath.isel(iline=5, offset=0).data.T.plot.imshow(ax=axs[0], **plot_kwargs)

# stack the no mute data
penobscot_3d_gath.isel(iline=5).data.mean("offset").T.plot.imshow(
    ax=axs[1], **plot_kwargs
)

# stack the muted data
penobscot_3d_gath_muted.isel(iline=5).data.mean("offset").T.plot.imshow(
    ax=axs[2], **plot_kwargs
)

fig, axs = plt.subplots(ncols=3, figsize=(20, 10)) plot_kwargs = dict(vmax=5000, interpolation="bicubic", yincrease=False) # compare with the zero offset trace and use imshow for interpolation penobscot_3d_gath.isel(iline=5, offset=0).data.T.plot.imshow(ax=axs[0], **plot_kwargs) # stack the no mute data penobscot_3d_gath.isel(iline=5).data.mean("offset").T.plot.imshow( ax=axs[1], **plot_kwargs ) # stack the muted data penobscot_3d_gath_muted.isel(iline=5).data.mean("offset").T.plot.imshow( ax=axs[2], **plot_kwargs )

Out[12]:

<matplotlib.image.AxesImage at 0x7fbae4b3e0b0>

In [13]:

!pip list

!pip list

Package                        Version           Editable project location
------------------------------ ----------------- ---------------------------------
addict                         2.4.0
anyio                          4.3.0
argon2-cffi                    23.1.0
argon2-cffi-bindings           21.2.0
arrow                          1.3.0
asttokens                      2.4.1
async-lru                      2.0.4
attrs                          23.2.0
Babel                          2.15.0
beautifulsoup4                 4.12.3
bleach                         6.1.0
bracex                         2.4
certifi                        2024.2.2
cffi                           1.16.0
charset-normalizer             3.3.2
click                          8.1.7
cloudpickle                    3.0.0
colorama                       0.4.6
comm                           0.2.2
contourpy                      1.2.1
cycler                         0.12.1
dask                           2024.5.0
debugpy                        1.8.1
decorator                      5.1.1
defusedxml                     0.7.1
distributed                    2024.5.0
exceptiongroup                 1.2.1
executing                      2.0.1
fastjsonschema                 2.19.1
fonttools                      4.51.0
fqdn                           1.5.1
fsspec                         2024.3.1
ghp-import                     2.1.0
griffe                         0.44.0
h11                            0.14.0
h5netcdf                       1.3.0
h5py                           3.11.0
httpcore                       1.0.5
httpx                          0.27.0
idna                           3.7
importlib_metadata             7.1.0
importlib_resources            6.4.0
ipykernel                      6.29.4
ipython                        8.24.0
isoduration                    20.11.0
jedi                           0.19.1
Jinja2                         3.1.4
json5                          0.9.25
jsonpointer                    2.4
jsonschema                     4.22.0
jsonschema-specifications      2023.12.1
jupyter_client                 8.6.1
jupyter_core                   5.7.2
jupyter-events                 0.10.0
jupyter-lsp                    2.2.5
jupyter_server                 2.14.0
jupyter_server_terminals       0.5.3
jupyterlab                     4.2.0
jupyterlab_pygments            0.3.0
jupyterlab_server              2.27.1
jupytext                       1.16.2
kiwisolver                     1.4.5
locket                         1.0.0
Markdown                       3.6
markdown-it-py                 3.0.0
MarkupSafe                     2.1.5
matplotlib                     3.8.4
matplotlib-inline              0.1.7
mdit-py-plugins                0.4.0
mdurl                          0.1.2
mergedeep                      1.3.4
mike                           2.1.1
mistune                        3.0.2
mkautodoc                      0.2.0
mkdocs                         1.6.0
mkdocs-autorefs                1.0.1
mkdocs-get-deps                0.2.0
mkdocs-include-markdown-plugin 6.0.6
mkdocs-jupyter                 0.24.7
mkdocs-material                9.5.21
mkdocs-material-extensions     1.3.1
mkdocs-simple-plugin           3.1.0
mkdocstrings                   0.25.1
mkdocstrings-python            1.10.0
more-itertools                 10.2.0
msgpack                        1.0.8
nbclient                       0.10.0
nbconvert                      7.16.4
nbformat                       5.10.4
nest-asyncio                   1.6.0
notebook_shim                  0.2.4
numpy                          1.26.4
overrides                      7.7.0
packaging                      24.0
paginate                       0.5.6
pandas                         2.2.2
pandocfilters                  1.5.1
parso                          0.8.4
partd                          1.4.2
pathspec                       0.12.1
pexpect                        4.9.0
pillow                         10.3.0
pip                            24.0
platformdirs                   4.2.1
pooch                          1.8.1
prometheus_client              0.20.0
prompt-toolkit                 3.0.43
psutil                         5.9.8
ptyprocess                     0.7.0
pure-eval                      0.2.2
pycparser                      2.22
Pygments                       2.18.0
pymdown-extensions             10.8.1
pyparsing                      3.1.2
pyshp                          2.3.1
python-dateutil                2.9.0.post0
python-json-logger             2.0.7
pytz                           2024.1
pyvista                        0.43.7
PyYAML                         6.0.1
pyyaml_env_tag                 0.1
pyzmq                          26.0.3
referencing                    0.35.1
regex                          2024.4.28
requests                       2.31.0
rfc3339-validator              0.1.4
rfc3986-validator              0.1.1
rpds-py                        0.18.1
scipy                          1.13.0
scooby                         0.10.0
segyio                         1.9.12
segysak                        0.5.dev46+unknown /home/runner/work/segysak/segysak
Send2Trash                     1.8.3
setuptools                     69.5.1
six                            1.16.0
sniffio                        1.3.1
sortedcontainers               2.4.0
soupsieve                      2.5
stack-data                     0.6.3
tblib                          3.0.0
terminado                      0.18.1
tinycss2                       1.3.0
tomli                          2.0.1
toolz                          0.12.1
tornado                        6.4
tqdm                           4.66.4
traitlets                      5.14.3
types-python-dateutil          2.9.0.20240316
typing_extensions              4.11.0
tzdata                         2024.1
uri-template                   1.3.0
urllib3                        2.2.1
verspec                        0.1.0
vtk                            9.3.0
watchdog                       4.0.0
wcmatch                        8.5.1
wcwidth                        0.2.13
webcolors                      1.13
webencodings                   0.5.1
websocket-client               1.8.0
wellpathpy                     0.5.0
wheel                          0.43.0
xarray                         2024.3.0
zict                           3.0.0
zipp                           3.18.1

In [ ]: